Counterfactual Neural Temporal Point Process for Estimating Causal
Influence of Misinformation on Social Media
- URL: http://arxiv.org/abs/2210.07518v1
- Date: Fri, 14 Oct 2022 05:00:10 GMT
- Title: Counterfactual Neural Temporal Point Process for Estimating Causal
Influence of Misinformation on Social Media
- Authors: Yizhou Zhang, Defu Cao, Yan Liu
- Abstract summary: We build up a causal framework that model the causal effect of misinformation from the perspective of temporal point process.
We apply our model on a real-world dataset of social media posts and engagements about COVID-19 vaccines.
- Score: 10.685717620191102
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent years have witnessed the rise of misinformation campaigns that spread
specific narratives on social media to manipulate public opinions on different
areas, such as politics and healthcare. Consequently, an effective and
efficient automatic methodology to estimate the influence of the misinformation
on user beliefs and activities is needed. However, existing works on
misinformation impact estimation either rely on small-scale psychological
experiments or can only discover the correlation between user behaviour and
misinformation. To address these issues, in this paper, we build up a causal
framework that model the causal effect of misinformation from the perspective
of temporal point process. To adapt the large-scale data, we design an
efficient yet precise way to estimate the Individual Treatment Effect(ITE) via
neural temporal point process and gaussian mixture models. Extensive
experiments on synthetic dataset verify the effectiveness and efficiency of our
model. We further apply our model on a real-world dataset of social media posts
and engagements about COVID-19 vaccines. The experimental results indicate that
our model recognized identifiable causal effect of misinformation that hurts
people's subjective emotions toward the vaccines.
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